Semantic Constraint Based Unsupervised Domain Adaptation for Cardiac Segmentation

Xin Wang; Fan Zhu; Yaxin Peng; Chaomin Shen; Zhen Ye; Chaozheng Zhou

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Semantic Constraint Based Unsupervised Domain Adaptation for Cardiac Segmentation

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摘要

The segmentation of unlabeled medical images is troublesome due to the high cost of annotation, and unsupervised domain adaptation is one solution to this. In this paper, an improved unsupervised domain adaptation method was proposed. The proposed method considered both global alignment and category-wise alignment. First, we aligned the appearance of two domains by image transformation. Second, we aligned the output maps of two domains in a global way. Then, we decomposed the semantic prediction map by category, aligning the prediction maps in a category-wise manner. Finally, we evaluated the proposed method on the 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, and obtained 82.1 on the dice similarity coefficient and 4.6 on the average symmetric surface distance, demonstrating the effectiveness of the combination of global alignment and category-wise alignment.

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《理论数学进展（英文）》 |2021年第6期|P.628-643|共16页
作者
Xin Wang; Fan Zhu; Yaxin Peng; Chaomin Shen; Zhen Ye; Chaozheng Zhou;
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作者单位

College of Science Shanghai University Shanghai China;

College of Science Shanghai University Shanghai China;

College of Science Shanghai University Shanghai China;

School of Computer Science and Technology East China Normal University Shanghai China;

Shanghai Electric Central Research Institute Shanghai China;

Shanghai Electric Central Research Institute Shanghai China;

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原文格式 PDF
正文语种 chi
中图分类自动化基础理论;
关键词
Medical Image Segmentation; Domain Adaptation; Category-Wise Alignment; Cardiac Segmentation;

Semantic Constraint Based Unsupervised Domain Adaptation for Cardiac Segmentation

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